library(phyloseq) # for phyloseq object
library(ggplot2)
library(cowplot)
library(tidyverse)
library("plotly") # plot 3D
library("microbiome") # for centered log-ratio
library("coda") # Aitchison distance
library("coda.base") # Aitchison distance
library("vegan") # NMDS
library(pheatmap) # for heatmap
# Set path
path <- "~/Projects/IBS_Meta-analysis_16S"
# Import phyloseq object
physeq.hugerth <- readRDS(file.path(path, "phyloseq-objects/physeq_hugerth.rds"))
# Sanity check
physeq.hugerth
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 5877 taxa and 525 samples ]
## sample_data() Sample Data: [ 525 samples by 19 sample variables ]
## tax_table() Taxonomy Table: [ 5877 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 5877 tips and 5875 internal nodes ]
## refseq() DNAStringSet: [ 5877 reference sequences ]
Phylogenetic tree was computed with the package phangorn, and the script was run on a cluster. Let’s check we have correctly generated a phylogenetic tree.
# Look at the tree
plot_tree(physeq.hugerth, color = "host_disease", ladderize="left")
This dataset has several covariates (gender, age, bmi, sample_storage_duration). We will check whether there is the same distribution of these covariates between healthy and IBS patients.
# Number of individuals in each group (keeping just 1 sample per individual)
metadata <- data.frame(sample_data(physeq.hugerth)) %>%
select(host_disease, host_bmi, host_age, host_sex, host_psy, host_ID) %>%
group_by(host_ID) %>%
summarise_all(first)
metadata %>%
count(host_disease)
# Age
metadata %>%
group_by(host_disease) %>%
summarize(mean_age=mean(host_age), sd_age=sd(host_age))
wilcox.test(metadata[metadata$host_disease == "IBS", ]$host_age,
metadata[metadata$host_disease == "Healthy", ]$host_age) # p=0.23
##
## Wilcoxon rank sum test with continuity correction
##
## data: metadata[metadata$host_disease == "IBS", ]$host_age and metadata[metadata$host_disease == "Healthy", ]$host_age
## W = 11154, p-value = 0.2311
## alternative hypothesis: true location shift is not equal to 0
# Gender
metadata %>%
count(host_disease, host_sex)
chisq.test(data.frame("Female" = c(164,53),
"Male" = c(123,32))) # p=0.47
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: data.frame(Female = c(164, 53), Male = c(123, 32))
## X-squared = 0.53372, df = 1, p-value = 0.465
# BMI
metadata %>%
group_by(host_disease) %>%
summarize(mean_bmi=mean(na.omit(host_bmi)), sd_bmi=sd(na.omit(host_bmi)))
wilcox.test(metadata[metadata$host_disease == "IBS",]$host_bmi,
metadata[metadata$host_disease == "Healthy", ]$host_bmi) # 0.07
##
## Wilcoxon rank sum test with continuity correction
##
## data: metadata[metadata$host_disease == "IBS", ]$host_bmi and metadata[metadata$host_disease == "Healthy", ]$host_bmi
## W = 10460, p-value = 0.06854
## alternative hypothesis: true location shift is not equal to 0
# Sample storage duration (take into account all samples per individual)
data.frame(sample_data(physeq.hugerth)) %>%
group_by(host_disease) %>%
summarize(mean_storage_duration = mean(sample_storage_duration),
sd = sd(sample_storage_duration))
wilcox.test(sample_data(physeq.hugerth)[sample_data(physeq.hugerth)$host_disease == "IBS", ]$sample_storage_duration,
sample_data(physeq.hugerth)[sample_data(physeq.hugerth)$host_disease == "Healthy", ]$sample_storage_duration) # p=0.07
##
## Wilcoxon rank sum test with continuity correction
##
## data: sample_data(physeq.hugerth)[sample_data(physeq.hugerth)$host_disease == "IBS", ]$sample_storage_duration and sample_data(physeq.hugerth)[sample_data(physeq.hugerth)$host_disease == "Healthy", ]$sample_storage_duration
## W = 22042, p-value = 0.07467
## alternative hypothesis: true location shift is not equal to 0
# Plot Phylum
plot_bar(physeq.hugerth, fill = "Phylum") + facet_wrap("host_disease", scales="free") +
theme(axis.text.x = element_text(size = 5))+
labs(x = "Samples", y = "Absolute abundance", title = "Hugerth dataset (2020)") +
ylim(0,200000)
# Plot Class
plot_bar(physeq.hugerth, fill = "Class")+ facet_wrap("host_disease", scales="free") +
theme(axis.text.x = element_text(size = 5))+
labs(x = "Samples", y = "Absolute abundance", title = "Hugerth dataset (2020)")+
ylim(0,200000)
Sequencing depth characteristics of the Hugerth dataset:
- minimum of 573 total count per sample
- median: 2.03410^{4} total count per sample
- maximum of 1.891810^{5} total count per sample
# Agglomerate to phylum & class levels
phylum.table <- physeq.hugerth %>%
tax_glom(taxrank = "Phylum") %>% # agglomerate at phylum level
transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance
psmelt() # Melt to long format
class.table <- physeq.hugerth %>%
tax_glom(taxrank = "Class") %>%
transform_sample_counts(function(x) {x/sum(x)} ) %>%
psmelt()
# Plot relative abundances
ggplot(phylum.table, aes(x = reorder(Sample, Sample, function(x) mean(phylum.table[Sample == x & Phylum == 'Bacteroidota', 'Abundance'])),
y = Abundance, fill = Phylum))+
facet_wrap(~ sample_type + host_disease, scales = "free") + # scales = "free" removes empty lines
geom_bar(stat = "identity") +
theme(axis.text.x = element_blank())+
labs(x = "Samples", y = "Relative abundance", title = "Hugerth dataset (2020)")
ggplot(class.table, aes(x = reorder(Sample, Sample, function(x) mean(class.table[Sample == x & Phylum == 'Bacteroidota', 'Abundance'])),
y = Abundance, fill = Class))+
facet_wrap(~ sample_type + host_disease, scales = "free") +
geom_bar(stat = "identity") +
theme(axis.text.x = element_blank(),
legend.key.size = unit(0.2, 'cm'),
legend.text = element_text(size=8))+
labs(x = "Samples", y = "Relative abundance", title = "Hugerth dataset (2020)")
# Extract abundance of only Bacteroidota and Firmicutes
relevant.covariates <- c('Sample', 'Abundance', 'host_disease', 'Phylum', 'sample_type', 'host_ID', 'host_age', 'host_sex', 'host_bmi')
bacter <- phylum.table %>%
filter(Phylum == "Bacteroidota") %>%
select(all_of(relevant.covariates)) %>%
dplyr::rename(Bacteroidota = Abundance) %>%
select(-Phylum)
firmi <- phylum.table %>%
filter(Phylum == "Firmicutes") %>%
select(all_of(relevant.covariates)) %>%
dplyr::rename(Firmicutes = Abundance) %>%
select(-Phylum)
# Calculate log2 ratio Firmicutes/Bacteroidota
ratio.FB <- left_join(x=bacter, y=firmi, by=c('Sample', 'host_disease', 'sample_type', 'host_ID', 'host_age', 'host_sex', 'host_bmi')) %>%
relocate(Firmicutes, .after=Bacteroidota) %>%
# Compute log ratios
mutate(logRatioFB = log2(Firmicutes/Bacteroidota))
# Plot log2 ratio Firmicutes/Bacteroidota
ggplot(ratio.FB, aes(x = host_disease, y = logRatioFB))+
geom_violin(aes(fill=host_disease))+
scale_fill_manual(values=scales::alpha(c("blue", "red"), .3))+
geom_jitter(width=0.1)+
facet_wrap(~sample_type) +
labs(x = "", y = 'Log2(Firmicutes/Bacteroidota)', title = "Firmicutes:Bacteroidota ratio")+
theme_cowplot()+
theme(legend.position="none")
# Statistical test sigmoid samples
wilcox.test(ratio.FB[ratio.FB$sample_type == "sigmoid" & ratio.FB$host_disease == "IBS","logRatioFB"],
ratio.FB[ratio.FB$sample_type == "sigmoid" & ratio.FB$host_disease == "Healthy","logRatioFB"]) # p = 0.6
##
## Wilcoxon rank sum test with continuity correction
##
## data: ratio.FB[ratio.FB$sample_type == "sigmoid" & ratio.FB$host_disease == "IBS", "logRatioFB"] and ratio.FB[ratio.FB$sample_type == "sigmoid" & ratio.FB$host_disease == "Healthy", "logRatioFB"]
## W = 10098, p-value = 0.5673
## alternative hypothesis: true location shift is not equal to 0
# Statistical test stool samples
wilcox.test(ratio.FB[ratio.FB$sample_type == "stool" & ratio.FB$host_disease == "IBS","logRatioFB"],
ratio.FB[ratio.FB$sample_type == "stool" & ratio.FB$host_disease == "Healthy","logRatioFB"]) # p=0.12
##
## Wilcoxon rank sum test with continuity correction
##
## data: ratio.FB[ratio.FB$sample_type == "stool" & ratio.FB$host_disease == "IBS", "logRatioFB"] and ratio.FB[ratio.FB$sample_type == "stool" & ratio.FB$host_disease == "Healthy", "logRatioFB"]
## W = 3306, p-value = 0.1229
## alternative hypothesis: true location shift is not equal to 0
# Paired data
ggplot(ratio.FB, aes(x = sample_type, y = logRatioFB))+
geom_violin(aes(fill=host_disease))+
scale_fill_manual(values=scales::alpha(c("blue", "red"), .3))+
geom_point()+
geom_line(aes(group=host_ID), lwd=0.1) +
facet_wrap(~host_disease) +
labs(x = "", y = 'Log2(Firmicutes/Bacteroidota)', title = "Paired data") +
theme_cowplot()+
theme(legend.position="none")
# Statistical test sigmoid vs stool samples
wilcox.test(ratio.FB[ratio.FB$sample_type == "stool" & ratio.FB$host_disease == "Healthy","logRatioFB"],
ratio.FB[ratio.FB$sample_type == "sigmoid" & ratio.FB$host_disease == "Healthy","logRatioFB"]) # p=0.1
##
## Wilcoxon rank sum test with continuity correction
##
## data: ratio.FB[ratio.FB$sample_type == "stool" & ratio.FB$host_disease == "Healthy", "logRatioFB"] and ratio.FB[ratio.FB$sample_type == "sigmoid" & ratio.FB$host_disease == "Healthy", "logRatioFB"]
## W = 15957, p-value = 0.09111
## alternative hypothesis: true location shift is not equal to 0
wilcox.test(ratio.FB[ratio.FB$sample_type == "stool" & ratio.FB$host_disease == "IBS","logRatioFB"],
ratio.FB[ratio.FB$sample_type == "sigmoid" & ratio.FB$host_disease == "IBS","logRatioFB"]) # p=0.3
##
## Wilcoxon rank sum test with continuity correction
##
## data: ratio.FB[ratio.FB$sample_type == "stool" & ratio.FB$host_disease == "IBS", "logRatioFB"] and ratio.FB[ratio.FB$sample_type == "sigmoid" & ratio.FB$host_disease == "IBS", "logRatioFB"]
## W = 1887.5, p-value = 0.2984
## alternative hypothesis: true location shift is not equal to 0
# Sanity check no sample with less than 500 total count
table(sample_sums(physeq.hugerth)<500) # all FALSE
#____________________________________________________________________
# PHYLOSEQ OBJECT WITH NON-ZERO COMPOSITIONS
physeq.NZcomp <- physeq.hugerth
otu_table(physeq.NZcomp)[otu_table(physeq.NZcomp) == 0] <- 0.5 # pseudocounts
# Sanity check that 0 values have been replaced
# otu_table(physeq.hugerth)[1:5,1:5]
# otu_table(physeq.NZcomp)[1:5,1:5]
# transform into compositions
physeq.NZcomp <- transform_sample_counts(physeq.NZcomp, function(x) x / sum(x) )
table(rowSums(otu_table(physeq.NZcomp))) # check if there is any row not summing to 1
# Save object
saveRDS(physeq.NZcomp, file.path(path, "data/analysis-individual/Hugerth-2019/02_EDA-Hugerth/physeq_NZcomp.rds"))
#____________________________________________________________________
# PHYLOSEQ OBJECT WITH RELATIVE COUNT (BETWEEN 0 AND 1)
physeq.rel <- physeq.hugerth
physeq.rel <- transform_sample_counts(physeq.rel, function(x) x / sum(x) )
# check the counts are all relative
# otu_table(physeq.hugerth)[1:5, 1:5]
# otu_table(physeq.rel)[1:5, 1:5]
# sanity check
table(rowSums(otu_table(physeq.rel))) # check if there is any row not summing to 1
# save the physeq.rel object
saveRDS(physeq.rel, file.path(path, "data/analysis-individual/Hugerth-2019/02_EDA-Hugerth/physeq_relative.rds"))
#____________________________________________________________________
# PHYLOSEQ OBJECT WITH COMMON-SCALE NORMALIZATION
physeq.CSN <- physeq.hugerth
physeq.CSN <- transform_sample_counts(physeq.CSN, function(x) (x*min(sample_sums(physeq.CSN))) / sum(x) )
# sanity check
table(rowSums(otu_table(physeq.CSN))) # check that all rows are summing to the same total
# save the physeq.CSN object
saveRDS(physeq.CSN, file.path(path, "data/analysis-individual/Hugerth-2019/02_EDA-Hugerth/physeq_CSN.rds"))
#____________________________________________________________________
# PHYLOSEQ OBJECT WITH CENTERED LOG RATIO COUNT
physeq.clr <- physeq.hugerth
physeq.clr <- microbiome::transform(physeq.hugerth, "clr") # the function adds pseudocounts itself
# Compare the otu tables in the original phyloseq object and the new one after CLR transformation
# otu_table(physeq.hugerth)[1:5, 1:5] # should contain absolute counts
# otu_table(physeq.clr)[1:5, 1:5] # should all be relative
# save the physeq.rel object
saveRDS(physeq.clr, file.path(path, "data/analysis-individual/Hugerth-2019/02_EDA-Hugerth/physeq_clr.rds"))
First, let’s look at these four distances of interest.
#____________________________________________________________________________________
# Measure distances
getDistances <- function(relPhyseq=physeq.rel, clrPhyseq=physeq.clr, csnPhyseq=physeq.CSN, nzcompPhyseq=physeq.NZcomp){
# sanity check
cat("nb samples relPhyseq:", nsamples(relPhyseq), "\n")
cat("nb samples clrPhyseq:", nsamples(clrPhyseq), "\n")
cat("nb samples csnPhyseq:", nsamples(csnPhyseq), "\n")
cat("nb samples nzcompPhyseq:", nsamples(nzcompPhyseq), "\n")
# Compute distances
print("Unifrac...")
set.seed(123) # for unifrac, need to set a seed
glom.UniF <- UniFrac(relPhyseq, weighted=TRUE, normalized=TRUE) # weighted unifrac
print("Aitchison...")
glom.ait <- phyloseq::distance(clrPhyseq, method = 'euclidean') # aitchison
print("Bray des bois...")
glom.bray <- phyloseq::distance(csnPhyseq, method = "bray") # bray-curtis
print("Canberra <3...")
glom.can <- phyloseq::distance(nzcompPhyseq, method = "canberra") # canberra
dist.list <- list("UniF" = glom.UniF, "Ait" = glom.ait, "Canb" = glom.can, "Bray" = glom.bray)
return(dist.list)
}
#____________________________________________________________________________________
# Plot in 2D the distances
plotDistances2D <- function(dlist, ordination="MDS", relPhyseq=physeq.rel, clrPhyseq=physeq.clr, csnPhyseq=physeq.CSN, nzcompPhyseq=physeq.NZcomp){
plist <- NULL
plist <- vector("list", 4)
names(plist) <- c("Weighted Unifrac", "Aitchison", "Bray-Curtis", "Canberra")
print("Unifrac")
# Weighted UniFrac
set.seed(123)
iMDS.UniF <- ordinate(relPhyseq, ordination, distance=dlist$UniF)
plist[[1]] <- plot_ordination(relPhyseq, iMDS.UniF, color="host_disease")
print("Aitchison")
# Aitchison
set.seed(123)
iMDS.Ait <- ordinate(clrPhyseq, ordination, distance=dlist$Ait)
plist[[2]] <- plot_ordination(clrPhyseq, iMDS.Ait, color="host_disease")
print("Bray")
# Bray-Curtis
set.seed(123)
iMDS.Bray <- ordinate(csnPhyseq, ordination, distance=dlist$Bray)
plist[[3]] <- plot_ordination(csnPhyseq, iMDS.Bray, color="host_disease")
print("Canberra")
# Canberra
set.seed(123)
iMDS.Can <- ordinate(nzcompPhyseq, ordination, distance=dlist$Can)
plist[[4]] <- plot_ordination(nzcompPhyseq, iMDS.Can, color="host_disease")
# Creating a dataframe to plot everything
plot.df = plyr::ldply(plist, function(x) x$data)
names(plot.df)[1] <- "distance"
return(plot.df)
}
Now let’s plot!
#________________
# FECAL DATA
#________________
# Get the distances & the plot data
dist.hugerth.fecal <- getDistances(relPhyseq = subset_samples(physeq.rel, sample_type=="stool"),
clrPhyseq = subset_samples(physeq.clr, sample_type=="stool"),
csnPhyseq = subset_samples(physeq.CSN, sample_type=="stool"),
nzcompPhyseq = subset_samples(physeq.NZcomp, sample_type=="stool"))
## nb samples relPhyseq: 174
## nb samples clrPhyseq: 174
## nb samples csnPhyseq: 174
## nb samples nzcompPhyseq: 174
## [1] "Unifrac..."
## [1] "Aitchison..."
## [1] "Bray des bois..."
## [1] "Canberra <3..."
plot.df.fecal <- plotDistances2D(dlist=dist.hugerth.fecal,
relPhyseq = subset_samples(physeq.rel, sample_type=="stool"),
clrPhyseq = subset_samples(physeq.clr, sample_type=="stool"),
csnPhyseq = subset_samples(physeq.CSN, sample_type=="stool"),
nzcompPhyseq = subset_samples(physeq.NZcomp, sample_type=="stool"))
## [1] "Unifrac"
## [1] "Aitchison"
## [1] "Bray"
## [1] "Canberra"
# Plot
ggplot(plot.df.fecal, aes(Axis.1, Axis.2, color=host_disease))+
geom_point(size=4, alpha=0.5) + scale_color_manual(values = c('blue', 'red'))+
facet_wrap(distance~., scales='free', nrow=1)+
theme_bw()+
theme(strip.text.x = element_text(size=20))+
labs(color="Disease", title="Fecal samples")
# ggsave(file.path(path.plots, "distances4_MDS_stool.jpg"), height = 4, width = 15)
#________________
# SIGMOID DATA
#________________
# Get the distances & the plot data
dist.hugerth.sigmoid <- getDistances(relPhyseq = subset_samples(physeq.rel, sample_type=="sigmoid"),
clrPhyseq = subset_samples(physeq.clr, sample_type=="sigmoid"),
csnPhyseq = subset_samples(physeq.CSN, sample_type=="sigmoid"),
nzcompPhyseq = subset_samples(physeq.NZcomp, sample_type=="sigmoid"))
## nb samples relPhyseq: 351
## nb samples clrPhyseq: 351
## nb samples csnPhyseq: 351
## nb samples nzcompPhyseq: 351
## [1] "Unifrac..."
## [1] "Aitchison..."
## [1] "Bray des bois..."
## [1] "Canberra <3..."
plot.df.sigmoid <- plotDistances2D(dlist=dist.hugerth.sigmoid,
relPhyseq = subset_samples(physeq.rel, sample_type=="sigmoid"),
clrPhyseq = subset_samples(physeq.clr, sample_type=="sigmoid"),
csnPhyseq = subset_samples(physeq.CSN, sample_type=="sigmoid"),
nzcompPhyseq = subset_samples(physeq.NZcomp, sample_type=="sigmoid"))
## [1] "Unifrac"
## [1] "Aitchison"
## [1] "Bray"
## [1] "Canberra"
# Plot
ggplot(plot.df.sigmoid, aes(Axis.1, Axis.2, color=host_disease))+
geom_point(size=4, alpha=0.5) + scale_color_manual(values = c('blue', 'red'))+
facet_wrap(distance~., scales='free', nrow=1)+
theme_bw()+
theme(strip.text.x = element_text(size=20))+
labs(color="Disease", title="Sigmoid samples")
# ggsave(file.path(path.plots, "distances4_MDS_sigmoid.jpg"), height = 4, width = 15)
#________________
# ALL DATA
#________________
# Get the distances & the plot data
dist.hugerth <- getDistances()
## nb samples relPhyseq: 525
## nb samples clrPhyseq: 525
## nb samples csnPhyseq: 525
## nb samples nzcompPhyseq: 525
## [1] "Unifrac..."
## [1] "Aitchison..."
## [1] "Bray des bois..."
## [1] "Canberra <3..."
plot.df <- plotDistances2D(dlist=dist.hugerth)
## [1] "Unifrac"
## [1] "Aitchison"
## [1] "Bray"
## [1] "Canberra"
# Plot
p1 <- ggplot(plot.df, aes(Axis.1, Axis.2, color=host_disease))+
geom_point(size=4, alpha=0.5) + scale_color_manual(values = c('blue', 'red'))+
facet_wrap(distance~., scales='free', nrow=1)+
theme_bw()+
theme(strip.text.x = element_text(size=20),
axis.title.x = element_blank())+
labs(color="Disease")
p2 <- ggplot(plot.df, aes(Axis.1, Axis.2, color=sample_type))+
geom_line(aes(group=host_ID), color="black", lwd=0.1)+
geom_point(size=4, alpha=0.5) + scale_color_manual(values = c('#33CC00', 'purple'))+
facet_wrap(distance~., scales='free', nrow=1)+
theme_bw()+
theme(strip.text.x = element_blank())+
labs(color="Sample type")
ggpubr::ggarrange(p1,p2, nrow=2)
# ggsave(file.path(path.plots, "distances4_MDS_all.jpg"), height = 8, width = 15)
For better visualization, we will also take a glance at reduction to 3D.
#____________________________________________________________________________________
# Plot 3D ordination
plotDistances3D <- function(d, name_dist, physeq=physeq.hugerth){
# Reset parameters
mds.3D <- NULL
xyz <- NULL
fig.3D <- NULL
# Reduce distance matrix to 3 dimensions
set.seed(123)
mds.3D <- metaMDS(d, method="MDS", k=3, trace = 0)
xyz <- scores(mds.3D, display="sites") # pull out the (x,y,z) coordinates
# Plot
fig.3D <- plot_ly(x=xyz[,1], y=xyz[,2], z=xyz[,3], type="scatter3d", mode="markers",
color=sample_data(physeq)$host_disease, colors = c("blue", "red"))%>%
layout(title = paste('MDS in 3D with', name_dist, 'distance', sep = ' '))
return(fig.3D)
}
Now let’s plot!
# Fecal samples
plotDistances3D(dist.hugerth.fecal$UniF, "UniFrac", subset_samples(physeq.hugerth, sample_type=="stool"))
plotDistances3D(dist.hugerth.fecal$Ait, "Aitchison", subset_samples(physeq.hugerth, sample_type=="stool"))
plotDistances3D(dist.hugerth.fecal$Canb, "Canberra", subset_samples(physeq.hugerth, sample_type=="stool"))
plotDistances3D(dist.hugerth.fecal$Bray, "Bray-Curtis", subset_samples(physeq.hugerth, sample_type=="stool"))
# Sigmoid samples
plotDistances3D(dist.hugerth.sigmoid$UniF, "UniFrac", subset_samples(physeq.hugerth, sample_type=="sigmoid"))
plotDistances3D(dist.hugerth.sigmoid$Ait, "Aitchison", subset_samples(physeq.hugerth, sample_type=="sigmoid"))
plotDistances3D(dist.hugerth.sigmoid$Canb, "Canberra", subset_samples(physeq.hugerth, sample_type=="sigmoid"))
plotDistances3D(dist.hugerth.sigmoid$Bray, "Bray-Curtis", subset_samples(physeq.hugerth, sample_type=="sigmoid"))
# For heatmaps: have group color
matcol <- data.frame(phenotype = sample_data(physeq.hugerth)[,"host_disease"],
sample = sample_data(physeq.hugerth)[,"sample_type"])
# Function to get heatmap from the distances computed
plotHeatmaps <- function(dlist, fontsize){
# Initialize variables
i=1
plist <- vector("list", 4)
names(plist) <- names(dlist)
# Loop through distances
for(d in dlist){
plist[[i]] <- pheatmap(as.matrix(d),
clustering_distance_rows = d,
clustering_distance_cols = d,
fontsize = fontsize,
show_rownames = F,
show_colnames = F,
annotation_col = matcol,
# annotation_row = matcol,
annotation_colors = list(host_disease = c('Healthy' = 'blue', 'IBS' = 'red'),
sample_type = c("stool" = "#33CC00", 'sigmoid' = 'purple')),
cluster_rows = T,
cluster_cols = T,
clustering_method = 'complete', # hc method
main = names(dlist)[i]) # have name of distance as title
i <- i+1
}
return(plist)
}
# Get the heatmaps
heatmp.hugerth <- plotHeatmaps(dlist = dist.hugerth, fontsize = 8)
#___________________________________________________________________
# FIGURE 3
library(reshape2)
# get useful metadata
metadata <- sample_data(physeq.hugerth) %>%
as_tibble() %>%
select(Run, host_disease, sample_type)
# Build dataframe with Bray-Curtis distance between IBS-HC, IBS-IBS, HC-HC
bc.comp <- melt(as.matrix(dist.hugerth$Bray), varnames = c("row", "col")) %>%
inner_join(metadata, by=c("row"="Run")) %>%
dplyr::rename(row_disease=host_disease, row_type=sample_type) %>%
inner_join(metadata, by=c("col"="Run")) %>%
dplyr::rename(col_disease=host_disease, col_type=sample_type) %>%
# Keep only distances between samples from same sample_type
filter(row_type == col_type) %>%
select(-col_type) %>%
dplyr::rename(sample_type=row_type) %>%
relocate(row_disease, .after=sample_type) %>%
# Classify comparison as between diseases, between Healthy, or between IBS samples
mutate(compare=ifelse(row_disease != col_disease, "Between",
ifelse(row_disease == "Healthy", "Healthy",
ifelse(row_disease == "IBS", "IBS", "Unknown")))) %>%
mutate(compare=factor(compare, levels=c("Between", "IBS", "Healthy")))
# Sanity check
bc.comp %>%
dplyr::count(row_disease, col_disease, compare)
# Plot
ggplot(bc.comp, aes(x=compare, y=value))+
facet_wrap(~sample_type, scales="free")+
geom_violin()+
geom_boxplot(width=0.4, outlier.shape = NA, notch=T)+
geom_jitter(size=0.1, width=0.05)+
theme_cowplot()+
labs(x='', y='Bray-Curtis dissimilarity')
# Calculate NMDS coordinates
set.seed(123)
NMDS.Bray.fecal <- ordinate(physeq = subset_samples(physeq.CSN, sample_type=="stool"),
method = "NMDS",
distance = dist.hugerth.fecal$Bray)
## Run 0 stress 0.225931
## Run 1 stress 0.2293622
## Run 2 stress 0.2285311
## Run 3 stress 0.2255545
## ... New best solution
## ... Procrustes: rmse 0.01992115 max resid 0.1154476
## Run 4 stress 0.2313091
## Run 5 stress 0.2450373
## Run 6 stress 0.235915
## Run 7 stress 0.2255749
## ... Procrustes: rmse 0.008081218 max resid 0.07390167
## Run 8 stress 0.2257507
## ... Procrustes: rmse 0.005270451 max resid 0.04131554
## Run 9 stress 0.2256218
## ... Procrustes: rmse 0.008101816 max resid 0.07879486
## Run 10 stress 0.234097
## Run 11 stress 0.2255944
## ... Procrustes: rmse 0.00838837 max resid 0.06781654
## Run 12 stress 0.2256381
## ... Procrustes: rmse 0.00861187 max resid 0.07966027
## Run 13 stress 0.2259211
## ... Procrustes: rmse 0.01998228 max resid 0.1156725
## Run 14 stress 0.226067
## Run 15 stress 0.2371384
## Run 16 stress 0.2262775
## Run 17 stress 0.228454
## Run 18 stress 0.2256025
## ... Procrustes: rmse 0.002503418 max resid 0.02473633
## Run 19 stress 0.2258836
## ... Procrustes: rmse 0.02064241 max resid 0.1145083
## Run 20 stress 0.2385753
## *** No convergence -- monoMDS stopping criteria:
## 6: no. of iterations >= maxit
## 14: stress ratio > sratmax
NMDS.Bray.sigmoid <- ordinate(physeq = subset_samples(physeq.CSN, sample_type=="sigmoid"),
method = "NMDS",
distance = dist.hugerth.sigmoid$Bray)
## Run 0 stress 0.2593207
## Run 1 stress 0.259414
## ... Procrustes: rmse 0.01411951 max resid 0.1904389
## Run 2 stress 0.2601868
## Run 3 stress 0.2615324
## Run 4 stress 0.2615583
## Run 5 stress 0.2619001
## Run 6 stress 0.2616245
## Run 7 stress 0.2669166
## Run 8 stress 0.2606921
## Run 9 stress 0.2592282
## ... New best solution
## ... Procrustes: rmse 0.01549034 max resid 0.2405753
## Run 10 stress 0.2605332
## Run 11 stress 0.2603586
## Run 12 stress 0.2601874
## Run 13 stress 0.259936
## Run 14 stress 0.2605008
## Run 15 stress 0.2601548
## Run 16 stress 0.2613746
## Run 17 stress 0.2622324
## Run 18 stress 0.2670312
## Run 19 stress 0.2606263
## Run 20 stress 0.2606027
## *** No convergence -- monoMDS stopping criteria:
## 12: no. of iterations >= maxit
## 8: stress ratio > sratmax
# Plot
p1 <- plot_ordination(subset_samples(physeq.CSN, sample_type=="stool"), NMDS.Bray.fecal, color="host_disease")+
geom_point(size=4, alpha=0.5) + scale_color_manual(values = c('blue', 'red'))+
theme_cowplot()+
theme(legend.position = "none")+
labs(title="Fecal samples")
p2 <- plot_ordination(subset_samples(physeq.CSN, sample_type=="sigmoid"), NMDS.Bray.sigmoid, color="host_disease")+
geom_point(size=4, alpha=0.5) + scale_color_manual(values = c('blue', 'red'))+
theme_cowplot()+
labs(color="Disease", title="Sigmoid samples")
ggpubr::ggarrange(p1,p2,ncol=2)
# Identify individuals that have both a sigmoid and a fecal sample
duo.samples <- sample_data(physeq.hugerth) %>%
as.data.frame() %>%
group_by(host_ID) %>%
filter(n_distinct(sample_type)>1) %>%
arrange(host_ID) %>%
ungroup()
# Sanity checks
# head(duo.samples) # sanity check
# length(duo.samples$Run)
# Get Chao1 richness
chao1 <- plot_richness(subset_samples(physeq.hugerth, Run %in% duo.samples$Run),
measures="Chao1")
chao1.df <- chao1$data %>%
select(Run, host_ID, sample_type, value) %>%
arrange(host_ID) %>%
pivot_wider(id_cols=host_ID, names_from=sample_type, values_from=value, values_fn=mean)
# Calculate NMDS coordinates
set.seed(123)
NMDS.Bray.comp <- ordinate(physeq = subset_samples(physeq.CSN, Run %in% duo.samples$Run),
method = "NMDS",
distance = "bray")
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.2014495
## Run 1 stress 0.2004157
## ... New best solution
## ... Procrustes: rmse 0.03447129 max resid 0.1815561
## Run 2 stress 0.2004676
## ... Procrustes: rmse 0.01962892 max resid 0.1205406
## Run 3 stress 0.1962415
## ... New best solution
## ... Procrustes: rmse 0.03437329 max resid 0.1844234
## Run 4 stress 0.1992742
## Run 5 stress 0.1990611
## Run 6 stress 0.2041989
## Run 7 stress 0.2051122
## Run 8 stress 0.2033499
## Run 9 stress 0.2018464
## Run 10 stress 0.2008418
## Run 11 stress 0.2066106
## Run 12 stress 0.2029382
## Run 13 stress 0.2038747
## Run 14 stress 0.2033621
## Run 15 stress 0.2038407
## Run 16 stress 0.2031519
## Run 17 stress 0.1971168
## Run 18 stress 0.2044534
## Run 19 stress 0.2012923
## Run 20 stress 0.2016106
## *** No convergence -- monoMDS stopping criteria:
## 11: no. of iterations >= maxit
## 9: stress ratio > sratmax
# Plot
p1 <- ggplot(chao1.df, aes(x=stool, y=sigmoid))+
geom_point(size=3)+
theme_cowplot()+
labs(x="Stool Chao1", y="Sigmoid biopsy Chao1", title="A")
p2 <- plot_ordination(subset_samples(physeq.CSN, Run %in% duo.samples$Run), NMDS.Bray.comp, color="sample_type")+
geom_line(aes(group=host_ID), color="grey", lwd=0.2)+
geom_point(size=4, alpha=0.5, shape=1) +
scale_color_manual(values = c('orange', 'red'))+
theme_cowplot()+
labs(color="Sample type", title="B")
ggpubr::ggarrange(p1,p2,ncol=2, widths = c(4,5))